Abstract
Background: Oral squamous cell carcinoma (OSCC) is increasing at an alarming rate particularly in low-income countries. This urges for research into noninvasive, user-friendly diagnostic tools that can be used in limited-resource settings. This study aims to test and validate the feasibility of e-nose technology for detecting OSCC in the limited-resource settings of the Sudanese population. Methods: Two e-nose devices (Aeonose™, eNose Company, Zutphen, The Netherlands) were used to collect breath samples from OSCC (n = 49) and control (n = 35) patients. Patients were divided into a training group for building an artificial neural network (ANN) model and a blinded control group for model validation. The Statistical Package for the Social Sciences (SPSS) software was used for the analysis of baseline characteristics and regression. Aethena proprietary software was used for data analysis using artificial neural networks based on patterns of volatile organic compounds. Results: A diagnostic accuracy of 81% was observed, with 88% sensitivity and 71% specificity. Conclusions: This study demonstrates that e-nose is an efficient tool for OSCC detection in limited-resource settings, where it offers a valuable cost-effective strategy to tackle the burden posed by OSCC.
Highlights
Gas chromatography-mass spectrometry (GC-MS) is the gold-standard platform that identifies individual volatile organic compounds (VOCs) according to their physical features when compared to a reference library
The healthy controls were selected among age- and sex-matched individuals who visited the out-patient clinic of the same hospital for routine dental treatments
The collection of breath samples did not result in any adverse effects
Summary
Disease-associated odor is an old phenomenon, which was first mentioned by Hippocrates of Kos (460–370 BC), who described “fetor oris” and “fetor hepaticus”. The interest for this phenomenon developed over time throughout Antoine Lavoisier studies in the. Oral squamous cell carcinoma (OSCC) is increasing at an alarming rate in low-income countries. This urges for research into noninvasive, user-friendly diagnostic tools that can be used in limited-resource settings. This study aims to test and validate the feasibility of e-nose technology for detecting OSCC in the limited-resource settings of the Sudanese population
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